The present invention relates to grammar authoring. More specifically, the present invention relates to use and authoring of an NLU system using a rules-based grammar and a statistical model.
In order to facilitate the development of speech enabled applications and services, semantic-based robust understanding systems are currently under development. Such systems are widely used in conversational, research systems. However, they are not particularly practical for use by conventional developers in implementing a conversational system. To a large extent, such implementations have relied on manual development of domain-specific grammars. This task is time consuming, error prone, and requires a significant amount of expertise in the domain.
In order to advance the development of speech enabled applications and services, an example-based grammar authoring tool has been introduced. The tool is known as SGStudio and is further discussed in Y. Wang and A. Acero, GRAMMAR LEARNING FOR SPOKEN LANGUAGE UNDERSTANDING, IEEE Workshop on Automatic Speech Recognition and Understanding, Madonna D. Campiglio Italy, 2001; and Y. Wang and A. Acero EVALUATION OF SPOKEN LANGUAGE GRAMMAR LEARNING IN ATIS DOMAIN, Proceedings of ICASSP, Orlando, Fla. 2002. This tool greatly eases grammar development by taking advantage of many different sources of prior information. It also allows a regular developer, with little linguistic knowledge, to build a semantic grammar for spoken language understanding. The system facilitates the semi-automatic generation of relatively high quality semantic grammars, with a small amount of data. Further, the tool not only significantly reduces the effort involved in developing a grammar, but also improves the understanding accuracy across different domains.
However, a purely rules-based grammar in a NLU system can still lack robustness and exhibit brittleness.